def main(args):
env = VectorEnv(
args.env, n_envs=args.n_envs, parallel=not args.serial, env_type=args.env_type
)
input_dim, action_dim, discrete, action_lim = get_env_properties(env, "mlp")
network = MlpActorCritic(
input_dim,
action_dim,
(1, 1), # layers
(1, 1),
"V", # type of value function
discrete,
action_lim=action_lim,
activation="relu",
)
generic_agent = A2C(network, env, rollout_size=args.rollout_size)
agent_parameter_choices = {
"gamma": [12, 121],
# 'clip_param': [0.2, 0.3],
# 'lr_policy': [0.001, 0.002],
# 'lr_value': [0.001, 0.002]
}
generate(
args.generations,
args.population,
agent_parameter_choices,
env,
generic_agent,
args,
)
def test_a2c():
env = VectorEnv("CartPole-v0", 1)
algo = A2C("mlp", env, rollout_size=128)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
def test_a2c_shared_discrete(self):
env = VectorEnv("CartPole-v0", 1)
algo = A2C("mlp", env, shared_layers=(32, 32), rollout_size=128)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
def test_a2c_cnn():
env = VectorEnv("Pong-v0", 1, env_type="atari")
algo = A2C("cnn", env, rollout_size=128)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
def test_a2c_continuous(self):
env = VectorEnv("Pendulum-v0", 1)
algo = A2C("mlp", env, rollout_size=128)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
trainer.evaluate()
shutil.rmtree("./logs")